Iwata Tatsuya, Okura Yuki, Saeki Maaki, Yoshikawa Takefumi
Department of Electrical and Electronic Engineering, Toyama Prefectural University, Imizu 939-0398, Japan.
Department of Information Systems Engineering, Toyama Prefectural University, Imizu 939-0398, Japan.
Sensors (Basel). 2024 May 6;24(9):2941. doi: 10.3390/s24092941.
This study proposes an optimization method for temperature modulation in chemiresistor-type gas sensors based on Bayesian optimization (BO), and its applicability was investigated. As voltage for a sensor heater, our previously proposed waveform was employed, and the parameters determining the voltage range were optimized. Employing the Bouldin-Davies index (DBI) as an objective function (OBJ), BO was utilized to minimize the DBI calculated from a feature matrix built from the collected data followed by pre-processing. The sensor responses were measured using five test gases with five concentrations, amounting to 2500 data points per parameter set. After seven trials with four initial parameter sets (ten parameter sets were tested in total), the DBI was successfully reduced from 2.1 to 1.5. The classification accuracy for the test gases based on the support vector machine tends to increase with decreasing the DBI, indicating that the DBI acts as a good OBJ. Additionally, the accuracy itself increased from 85.4% to 93.2% through optimization. The deviation from the tendency that the accuracy increases with decreasing the DBI for some parameter sets was also discussed. Consequently, it was demonstrated that the proposed optimization method based on BO is promising for temperature modulation.
本研究提出了一种基于贝叶斯优化（BO）的化学电阻型气体传感器温度调制优化方法，并对其适用性进行了研究。作为传感器加热器的电压，采用了我们先前提出的波形，并对确定电压范围的参数进行了优化。以Bouldin-Davies指数（DBI）作为目标函数（OBJ），利用BO使根据预处理后的采集数据构建的特征矩阵计算出的DBI最小化。使用五种浓度的五种测试气体测量传感器响应，每个参数集有2500个数据点。在对四个初始参数集进行七次试验后（总共测试了十个参数集），DBI成功从2.1降至1.5。基于支持向量机的测试气体分类准确率倾向于随着DBI的降低而提高，这表明DBI是一个良好的OBJ。此外，通过优化，准确率本身从85.4%提高到了93.2%。还讨论了某些参数集偏离准确率随DBI降低而提高这一趋势的情况。结果表明，所提出的基于BO的优化方法在温度调制方面具有前景。
